| Year | Population | Yearly % Change | Yearly Change | Migrants (net) | Median Age | Fertility Rate | Density (P/Km²) | Urban Pop % | Urban Population | Country's Share of World Pop | World Population | China Global Rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020 | 1439323776 | 0.39 | 5540090 | -348399 | 38.4 | 1.69 | 153 | 60.8 | 875075919 | 18.47 | 7794798739 | 1 |
| 1 | 2019 | 1433783686 | 0.43 | 6135900 | -348399 | 37.0 | 1.65 | 153 | 59.7 | 856409297 | 18.59 | 7713468100 | 1 |
| 2 | 2018 | 1427647786 | 0.47 | 6625995 | -348399 | 37.0 | 1.65 | 152 | 58.6 | 837022095 | 18.71 | 7631091040 | 1 |
| 3 | 2017 | 1421021791 | 0.49 | 6972440 | -348399 | 37.0 | 1.65 | 151 | 57.5 | 816957613 | 18.83 | 7547858925 | 1 |
| 4 | 2016 | 1414049351 | 0.51 | 7201481 | -348399 | 37.0 | 1.65 | 151 | 56.3 | 796289491 | 18.94 | 7464022049 | 1 |
| 5 | 2015 | 1406847870 | 0.55 | 7607451 | -310442 | 36.7 | 1.64 | 150 | 55.1 | 775352918 | 19.06 | 7379797139 | 1 |
| 6 | 2010 | 1368810615 | 0.57 | 7606847 | -435677 | 35.0 | 1.62 | 146 | 48.9 | 669353557 | 19.68 | 6956823603 | 1 |
| 7 | 2005 | 1330776380 | 0.62 | 8045123 | -393116 | 32.6 | 1.61 | 142 | 42.2 | 561983323 | 20.34 | 6541907027 | 1 |
| 8 | 2000 | 1290550765 | 0.79 | 9926046 | -76600 | 30.0 | 1.62 | 137 | 35.7 | 460377048 | 21.01 | 6143493823 | 1 |
| 9 | 1995 | 1240920535 | 1.07 | 12807372 | -155996 | 27.4 | 1.83 | 132 | 30.9 | 383901711 | 21.60 | 5744212979 | 1 |
| 10 | 1990 | 1176883674 | 1.82 | 20258863 | -86330 | 24.9 | 2.73 | 125 | 26.3 | 310022147 | 22.09 | 5327231061 | 1 |
| 11 | 1985 | 1075589361 | 1.47 | 15100025 | -40000 | 23.5 | 2.52 | 115 | 22.8 | 244946241 | 22.08 | 4870921740 | 1 |
| 12 | 1980 | 1000089235 | 1.55 | 14769670 | -9401 | 21.9 | 3.01 | 107 | 19.2 | 192392094 | 22.43 | 4458003514 | 1 |
| 13 | 1975 | 926240885 | 2.28 | 19727898 | -221096 | 20.3 | 4.85 | 99 | 17.3 | 160244444 | 22.70 | 4079480606 | 1 |
| 14 | 1970 | 827601394 | 2.70 | 20676485 | -32000 | 19.3 | 6.30 | 88 | 17.3 | 143513192 | 22.36 | 3700437046 | 1 |
| 15 | 1965 | 724218968 | 1.86 | 12762182 | -225145 | 19.8 | 6.15 | 77 | 18.0 | 130684595 | 21.69 | 3339583597 | 1 |
| 16 | 1960 | 660408056 | 1.53 | 9633300 | -11900 | 21.3 | 5.48 | 70 | 16.1 | 106561743 | 21.76 | 3034949748 | 1 |
| 17 | 1955 | 612241554 | 2.00 | 11564456 | -51205 | 22.2 | 6.11 | 65 | 13.8 | 84639825 | 22.08 | 2773019936 | 1 |
(1154278093.4444444, 1265735650.0, 292216785.8696303)
(-210605.72222222222, -223120.5, 151843.0224524872)
(466984847.3888889, 422139379.5, 304409602.7372602)
I have explored the statistics on population, migrants (net) and urban population mean, median and standard diviation.
Year 0 Population 0 Yearly % Change 0 Yearly Change 0 Migrants (net) 0 Median Age 0 Fertility Rate 0 Density (P/Km²) 0 Urban Pop % 0 Urban Population 0 Country's Share of World Pop 0 World Population 0 China Global Rank 0 dtype: int64
As you see, there are no NaNs and data seems to be pretty clean in general.
| Year | Population | Yearly % Change | Yearly Change | Migrants (net) | Median Age | Fertility Rate | Density (P/Km²) | Urban Pop % | Urban Population | Country's Share of World Pop | World Population | China Global Rank | Urban index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020 | 1439323776 | 0.39 | 5540090 | -348399 | 38.4 | 1.69 | 153 | 60.8 | 875075919 | 18.47 | 7794798739 | 1 | 2.516447 |
| 1 | 2019 | 1433783686 | 0.43 | 6135900 | -348399 | 37.0 | 1.65 | 153 | 59.7 | 856409297 | 18.59 | 7713468100 | 1 | 2.562814 |
| 2 | 2018 | 1427647786 | 0.47 | 6625995 | -348399 | 37.0 | 1.65 | 152 | 58.6 | 837022095 | 18.71 | 7631091040 | 1 | 2.593857 |
| 3 | 2017 | 1421021791 | 0.49 | 6972440 | -348399 | 37.0 | 1.65 | 151 | 57.5 | 816957613 | 18.83 | 7547858925 | 1 | 2.626087 |
| 4 | 2016 | 1414049351 | 0.51 | 7201481 | -348399 | 37.0 | 1.65 | 151 | 56.3 | 796289491 | 18.94 | 7464022049 | 1 | 2.682060 |
| 5 | 2015 | 1406847870 | 0.55 | 7607451 | -310442 | 36.7 | 1.64 | 150 | 55.1 | 775352918 | 19.06 | 7379797139 | 1 | 2.722323 |
| 6 | 2010 | 1368810615 | 0.57 | 7606847 | -435677 | 35.0 | 1.62 | 146 | 48.9 | 669353557 | 19.68 | 6956823603 | 1 | 2.985685 |
| 7 | 2005 | 1330776380 | 0.62 | 8045123 | -393116 | 32.6 | 1.61 | 142 | 42.2 | 561983323 | 20.34 | 6541907027 | 1 | 3.364929 |
| 8 | 2000 | 1290550765 | 0.79 | 9926046 | -76600 | 30.0 | 1.62 | 137 | 35.7 | 460377048 | 21.01 | 6143493823 | 1 | 3.837535 |
| 9 | 1995 | 1240920535 | 1.07 | 12807372 | -155996 | 27.4 | 1.83 | 132 | 30.9 | 383901711 | 21.60 | 5744212979 | 1 | 4.271845 |
| 10 | 1990 | 1176883674 | 1.82 | 20258863 | -86330 | 24.9 | 2.73 | 125 | 26.3 | 310022147 | 22.09 | 5327231061 | 1 | 4.752852 |
| 11 | 1985 | 1075589361 | 1.47 | 15100025 | -40000 | 23.5 | 2.52 | 115 | 22.8 | 244946241 | 22.08 | 4870921740 | 1 | 5.043860 |
| 12 | 1980 | 1000089235 | 1.55 | 14769670 | -9401 | 21.9 | 3.01 | 107 | 19.2 | 192392094 | 22.43 | 4458003514 | 1 | 5.572917 |
| 13 | 1975 | 926240885 | 2.28 | 19727898 | -221096 | 20.3 | 4.85 | 99 | 17.3 | 160244444 | 22.70 | 4079480606 | 1 | 5.722543 |
| 14 | 1970 | 827601394 | 2.70 | 20676485 | -32000 | 19.3 | 6.30 | 88 | 17.3 | 143513192 | 22.36 | 3700437046 | 1 | 5.086705 |
| 15 | 1965 | 724218968 | 1.86 | 12762182 | -225145 | 19.8 | 6.15 | 77 | 18.0 | 130684595 | 21.69 | 3339583597 | 1 | 4.277778 |
| 16 | 1960 | 660408056 | 1.53 | 9633300 | -11900 | 21.3 | 5.48 | 70 | 16.1 | 106561743 | 21.76 | 3034949748 | 1 | 4.347826 |
| 17 | 1955 | 612241554 | 2.00 | 11564456 | -51205 | 22.2 | 6.11 | 65 | 13.8 | 84639825 | 22.08 | 2773019936 | 1 | 4.710145 |
I call "urban index" density devided by percentage of urban population%. It in some way shows the urbanization of a country.
| Year | Population | Yearly % Change | Yearly Change | Migrants (net) | Median Age | Fertility Rate | Density (P/Km²) | Urban Pop % | Urban Population | Country's Share of World Pop | World Population | China Global Rank | Urban index | Migrant index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020 | 1439323776 | 0.39 | 5540090 | -348399 | 38.4 | 1.69 | 153 | 60.8 | 875075919 | 18.47 | 7794798739 | 1 | 2.516447 | -0.062887 |
| 1 | 2019 | 1433783686 | 0.43 | 6135900 | -348399 | 37.0 | 1.65 | 153 | 59.7 | 856409297 | 18.59 | 7713468100 | 1 | 2.562814 | -0.056780 |
| 2 | 2018 | 1427647786 | 0.47 | 6625995 | -348399 | 37.0 | 1.65 | 152 | 58.6 | 837022095 | 18.71 | 7631091040 | 1 | 2.593857 | -0.052581 |
| 3 | 2017 | 1421021791 | 0.49 | 6972440 | -348399 | 37.0 | 1.65 | 151 | 57.5 | 816957613 | 18.83 | 7547858925 | 1 | 2.626087 | -0.049968 |
| 4 | 2016 | 1414049351 | 0.51 | 7201481 | -348399 | 37.0 | 1.65 | 151 | 56.3 | 796289491 | 18.94 | 7464022049 | 1 | 2.682060 | -0.048379 |
| 5 | 2015 | 1406847870 | 0.55 | 7607451 | -310442 | 36.7 | 1.64 | 150 | 55.1 | 775352918 | 19.06 | 7379797139 | 1 | 2.722323 | -0.040808 |
| 6 | 2010 | 1368810615 | 0.57 | 7606847 | -435677 | 35.0 | 1.62 | 146 | 48.9 | 669353557 | 19.68 | 6956823603 | 1 | 2.985685 | -0.057274 |
| 7 | 2005 | 1330776380 | 0.62 | 8045123 | -393116 | 32.6 | 1.61 | 142 | 42.2 | 561983323 | 20.34 | 6541907027 | 1 | 3.364929 | -0.048864 |
| 8 | 2000 | 1290550765 | 0.79 | 9926046 | -76600 | 30.0 | 1.62 | 137 | 35.7 | 460377048 | 21.01 | 6143493823 | 1 | 3.837535 | -0.007717 |
| 9 | 1995 | 1240920535 | 1.07 | 12807372 | -155996 | 27.4 | 1.83 | 132 | 30.9 | 383901711 | 21.60 | 5744212979 | 1 | 4.271845 | -0.012180 |
| 10 | 1990 | 1176883674 | 1.82 | 20258863 | -86330 | 24.9 | 2.73 | 125 | 26.3 | 310022147 | 22.09 | 5327231061 | 1 | 4.752852 | -0.004261 |
| 11 | 1985 | 1075589361 | 1.47 | 15100025 | -40000 | 23.5 | 2.52 | 115 | 22.8 | 244946241 | 22.08 | 4870921740 | 1 | 5.043860 | -0.002649 |
| 12 | 1980 | 1000089235 | 1.55 | 14769670 | -9401 | 21.9 | 3.01 | 107 | 19.2 | 192392094 | 22.43 | 4458003514 | 1 | 5.572917 | -0.000637 |
| 13 | 1975 | 926240885 | 2.28 | 19727898 | -221096 | 20.3 | 4.85 | 99 | 17.3 | 160244444 | 22.70 | 4079480606 | 1 | 5.722543 | -0.011207 |
| 14 | 1970 | 827601394 | 2.70 | 20676485 | -32000 | 19.3 | 6.30 | 88 | 17.3 | 143513192 | 22.36 | 3700437046 | 1 | 5.086705 | -0.001548 |
| 15 | 1965 | 724218968 | 1.86 | 12762182 | -225145 | 19.8 | 6.15 | 77 | 18.0 | 130684595 | 21.69 | 3339583597 | 1 | 4.277778 | -0.017642 |
| 16 | 1960 | 660408056 | 1.53 | 9633300 | -11900 | 21.3 | 5.48 | 70 | 16.1 | 106561743 | 21.76 | 3034949748 | 1 | 4.347826 | -0.001235 |
| 17 | 1955 | 612241554 | 2.00 | 11564456 | -51205 | 22.2 | 6.11 | 65 | 13.8 | 84639825 | 22.08 | 2773019936 | 1 | 4.710145 | -0.004428 |
Migrant index is pretty basic. It clearifies the role of migrants in yearly change.
<AxesSubplot: xlabel='Year', ylabel='Population'>
It shows us that population is increasing consistently.
<BarContainer object of 18 artists>
The bar basicly shows that with population also grows median age
<AxesSubplot: xlabel='Year', ylabel='Fertility Rate'>
Graph potrays the plunge of fertility over the years